A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.
Project description
SelfClean
A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.
Development Environment
Run make
for a list of possible targets.
Installation
Run these commands to install the project:
make init
make install
To run linters on all files:
pre-commit run --all-files
Code and test conventions
black
for code styleisort
for import sortingpytest
for running tests
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
selfclean-0.0.2.tar.gz
(15.1 kB
view hashes)
Built Distribution
selfclean-0.0.2-py3-none-any.whl
(20.9 kB
view hashes)
Close
Hashes for selfclean-0.0.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a4e2b64b49bfb3dff87548b445cd77ce233dc168753c434b2c50e28891aa2cf9 |
|
MD5 | 5bb1b073680abc51fd92fac9db2f1c0e |
|
BLAKE2b-256 | 1c08034e59be791019cbf37de70bb229b96eedb6206e349bbd91a551bdbbd591 |